Failed

robust.testing.t_legacy.TestLegacy.test_simple_wing (from robust.testing.t_legacy.TestLegacy-20190924142948)

Error Message

Geometric Program is not fully bounded:   C_f has no upper bound

Stacktrace

Traceback (most recent call last):
  File "/jenkins/workspace/robust_PullRequest/mosek/robust/testing/t_legacy.py", line 42, in test_simple_wing
    nominal_number_of_constraints, directly_uncertain_vars_subs)
  File "/jenkins/workspace/robust_PullRequest/mosek/robust/simulations/simulate.py", line 172, in print_variable_gamma_results
    number_of_time_average_solves)
  File "/jenkins/workspace/robust_PullRequest/mosek/robust/simulations/simulate.py", line 89, in simulate_robust_model
    linearizationTolerance=linearization_tolerance)
  File "/jenkins/workspace/robust_PullRequest/mosek/robust/robust.py", line 235, in robustsolve
    self.setup(verbosity, **options)
  File "/jenkins/workspace/robust_PullRequest/mosek/robust/robust.py", line 207, in setup
    feasible=True)
  File "/jenkins/workspace/robust_PullRequest/mosek/robust/robust.py", line 417, in find_number_of_piece_wise_linearization
    sol_upper = RobustModel.internalsolve(model_upper, verbosity=0)
  File "/jenkins/workspace/robust_PullRequest/mosek/robust/robust.py", line 454, in internalsolve
    return model.solve(verbosity=verbosity)
  File "/jenkins/workspace/robust_PullRequest/mosek/gpkit/gpkit/constraints/prog_factories.py", line 123, in solvefn
    self.program, progsolve = genfunction(self)
  File "/jenkins/workspace/robust_PullRequest/mosek/gpkit/gpkit/constraints/prog_factories.py", line 80, in programify
    prog = program(self.cost, self, constants, **kwargs)
  File "/jenkins/workspace/robust_PullRequest/mosek/gpkit/gpkit/constraints/gp.py", line 112, in __init__
    + boundstrs)
ValueError: Geometric Program is not fully bounded:
  C_f has no upper bound
		

Standard Output

SP is not converging! Last GP iteration had a higher cost (3e+03) than the previous one (2.7e+03). Results for each iteration are in (Model).program.results. If your model contains SignomialEqualities, note that convergence is not guaranteed: try replacing any SigEqs you can and solving again.
SP is not converging! Last GP iteration had a higher cost (3e+03) than the previous one (2.7e+03). Results for each iteration are in (Model).program.results. If your model contains SignomialEqualities, note that convergence is not guaranteed: try replacing any SigEqs you can and solving again.
SP is not converging! Last GP iteration had a higher cost (3e+03) than the previous one (2.7e+03). Results for each iteration are in (Model).program.results. If your model contains SignomialEqualities, note that convergence is not guaranteed: try replacing any SigEqs you can and solving again.
SP is not converging! Last GP iteration had a higher cost (2.9e+03) than the previous one (2.9e+03). Results for each iteration are in (Model).program.results. If your model contains SignomialEqualities, note that convergence is not guaranteed: try replacing any SigEqs you can and solving again.
SP is not converging! Last GP iteration had a higher cost (2.9e+03) than the previous one (2.9e+03). Results for each iteration are in (Model).program.results. If your model contains SignomialEqualities, note that convergence is not guaranteed: try replacing any SigEqs you can and solving again.
SP is not converging! Last GP iteration had a higher cost (3e+03) than the previous one (2.9e+03). Results for each iteration are in (Model).program.results. If your model contains SignomialEqualities, note that convergence is not guaranteed: try replacing any SigEqs you can and solving again.
SP is not converging! Last GP iteration had a higher cost (3e+03) than the previous one (3e+03). Results for each iteration are in (Model).program.results. If your model contains SignomialEqualities, note that convergence is not guaranteed: try replacing any SigEqs you can and solving again.
SP is not converging! Last GP iteration had a higher cost (3.3e+03) than the previous one (3.3e+03). Results for each iteration are in (Model).program.results. If your model contains SignomialEqualities, note that convergence is not guaranteed: try replacing any SigEqs you can and solving again.
SP is not converging! Last GP iteration had a higher cost (3.3e+03) than the previous one (3.3e+03). Results for each iteration are in (Model).program.results. If your model contains SignomialEqualities, note that convergence is not guaranteed: try replacing any SigEqs you can and solving again.
SP is not converging! Last GP iteration had a higher cost (3.4e+03) than the previous one (3.4e+03). Results for each iteration are in (Model).program.results. If your model contains SignomialEqualities, note that convergence is not guaranteed: try replacing any SigEqs you can and solving again.
SP is not converging! Last GP iteration had a higher cost (3.2e+03) than the previous one (3.2e+03). Results for each iteration are in (Model).program.results. If your model contains SignomialEqualities, note that convergence is not guaranteed: try replacing any SigEqs you can and solving again.
SP is not converging! Last GP iteration had a higher cost (3.3e+03) than the previous one (3.3e+03). Results for each iteration are in (Model).program.results. If your model contains SignomialEqualities, note that convergence is not guaranteed: try replacing any SigEqs you can and solving again.
SP is not converging! Last GP iteration had a higher cost (3.3e+03) than the previous one (3.3e+03). Results for each iteration are in (Model).program.results. If your model contains SignomialEqualities, note that convergence is not guaranteed: try replacing any SigEqs you can and solving again.
SP is not converging! Last GP iteration had a higher cost (3.9e+03) than the previous one (3.9e+03). Results for each iteration are in (Model).program.results. If your model contains SignomialEqualities, note that convergence is not guaranteed: try replacing any SigEqs you can and solving again.
SP is not converging! Last GP iteration had a higher cost (3.8e+03) than the previous one (3.8e+03). Results for each iteration are in (Model).program.results. If your model contains SignomialEqualities, note that convergence is not guaranteed: try replacing any SigEqs you can and solving again.
SP is not converging! Last GP iteration had a higher cost (3.8e+03) than the previous one (3.8e+03). Results for each iteration are in (Model).program.results. If your model contains SignomialEqualities, note that convergence is not guaranteed: try replacing any SigEqs you can and solving again.
SP is not converging! Last GP iteration had a higher cost (3.9e+03) than the previous one (3.9e+03). Results for each iteration are in (Model).program.results. If your model contains SignomialEqualities, note that convergence is not guaranteed: try replacing any SigEqs you can and solving again.
SP is not converging! Last GP iteration had a higher cost (3.9e+03) than the previous one (3.9e+03). Results for each iteration are in (Model).program.results. If your model contains SignomialEqualities, note that convergence is not guaranteed: try replacing any SigEqs you can and solving again.
SP is not converging! Last GP iteration had a higher cost (3.9e+03) than the previous one (3.9e+03). Results for each iteration are in (Model).program.results. If your model contains SignomialEqualities, note that convergence is not guaranteed: try replacing any SigEqs you can and solving again.
SP is not converging! Last GP iteration had a higher cost (3e+03) than the previous one (2.7e+03). Results for each iteration are in (Model).program.results. If your model contains SignomialEqualities, note that convergence is not guaranteed: try replacing any SigEqs you can and solving again.
SP is not converging! Last GP iteration had a higher cost (4.5e+03) than the previous one (4e+03). Results for each iteration are in (Model).program.results. If your model contains SignomialEqualities, note that convergence is not guaranteed: try replacing any SigEqs you can and solving again.
SP is not converging! Last GP iteration had a higher cost (4.6e+03) than the previous one (4.5e+03). Results for each iteration are in (Model).program.results. If your model contains SignomialEqualities, note that convergence is not guaranteed: try replacing any SigEqs you can and solving again.
SP is not converging! Last GP iteration had a higher cost (3e+03) than the previous one (2.7e+03). Results for each iteration are in (Model).program.results. If your model contains SignomialEqualities, note that convergence is not guaranteed: try replacing any SigEqs you can and solving again.
SP is not converging! Last GP iteration had a higher cost (3e+03) than the previous one (2.7e+03). Results for each iteration are in (Model).program.results. If your model contains SignomialEqualities, note that convergence is not guaranteed: try replacing any SigEqs you can and solving again.
	

Standard Error